CSCI 343 Quiz 5 Flashcards

1
Q

demographic data

A

consumers’ demographic info in social media (age, gender, education, geography, etc.)

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2
Q

product data

A

product data in social media, which are generated when a particular brand or product name is mentioned

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3
Q

psychographic data

A

the type of social media data that indicates consumers’ personality, interest, and lifestyle

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4
Q

behavioral data

A

past behavioral information in social media that supports to predict future action

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5
Q

referrals data

A

ratings, reviews, and non-verbal attitudes generate referral data

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6
Q

location data

A

real time location data of consumer

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7
Q

intention data

A

data in social media that indicates consumers’ future buying intentions and activity

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8
Q

topic modeling definition

A

detecting dominant themes or topics by sifting through a large body of captured text

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9
Q

purpose of topic modeling

A

helps to identify underlying themes/topics

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10
Q

opinion mining definition

A

focuses on the views, beliefs, and judgement rather than considering positive or negative sentiment

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11
Q

purpose of opinion mining

A

measures the views and beliefs based on the criteria that depend on the purpose of analysis

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12
Q

sentiment analysis definition

A

interpretation of data of public/consumer/user sentiments, evaluations, appraisals, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes

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13
Q

purpose of sentiment analysis

A

measures the individual, group, community’s emotions toward any types of events, products, services, brands, etc.

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14
Q

social network analysis definition

A

analysis of the social network made up of individuals called nodes and connected with other nodes with similar interest; data analysis technique includes number of nodes, frequency of edges

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15
Q

social network analysis is the only type of social media analysis that

A

looks more outward

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16
Q

purpose of social network analysis

A

measures the types and depth of relationship between the networks; predict a person’s beliefs based on their network

17
Q

trend analysis definition

A

predicting market trends or customer behavior using historical data

18
Q

purpose of trend analysis

A

forecasting sales, market share, customer growth, or movements in the stock market

19
Q

popularity prediction definition

A

collecting positive and negative opinion/ranks/feedbacks, shares and likes on certain subjects or events and to understand the level of current popularity and forecast the future based on the current evidence

20
Q

purpose of popularity prediction

A

allows organizations to forecast the future demand of products, services, or events

21
Q

customer engagement analysis definition

A

the process to prolong the conversation or events or activities with social media participants or users; without proper incentives it is difficult to create engagement for a long time

22
Q

purpose of customer engagement analysis

A

measure the success of the online activities; helps organization to understand the current situation and next action needed to be successful online

23
Q

visual analytics definition

A

iterative process that involves information gathering, processing, and decision making

24
Q

purpose of visual analytics

A

use graphical interfaces to present, explore, and confirm relationships among variables

25
use bar charts to
compare data across categories
26
use line charts to
view trends in data, usually over time (show a value evolving)
27
instead of making a pie chart the focus of your dashboard, try using them to
drill down on other visualizations (add information)
28
use maps to
show how location correlates with trends in your data
29
use scatter plots to
investigate the relationship between different variables
30
scatter plots are an effective way to give you a sense of
trends, concentrations, and outliers
31
use Gantt charts to
display a project schedule or show changes in use or activity over time
32
use bubble charts to
show the relationship between three or more measures
33
use histograms to
understand the distribution of your data (how data falls into categories)
34
use a bullet hart to
quickly compare a metric to a goal ("how are we doing")
35
use heat maps to
compare categorical data using color (best for presenting data sets with lots of categories)
36
use highlight tables to
draw attention to detailed information
37
use treemaps to
show data with multiple levels of categorization
38
use box-and-whisker plots to
show the distribution of a data set; see how data is skewed towards one end and identify outliers
39
use a candlestick chart for
showing financial data